Growth, Employment, Inequality, and the Environment

Unity of Knowledge in Economics: Volume I

A. Figueroa

• Publisher: Palgrave Macmillan
• Year: 2015
• ISBN:
  9781137502674
• Topic: Economics
• Subtopic: Economic Theory
• Index: Economics

Growth, Employment, Inequality, and the Environment deals with the fundamental economic problems of our time: employment, inequality, the environment, and quality of life. This exciting new volume is unique in that it is the first book of its kind in which these problems are analyzed using a unified theory framework.Figueroa achieves his goal by addressing two significant problems. First, to solve the epistemological challenges of building unity of knowledge, he presents a unified theory of capitalism. Second, he considers the epistemological problem of the role of theory in scientific knowledge. This book therefore deals with a consistent theoretical system. That having been said, these theories which contain logically correct propositions may turn out to be empirically false. In order to avoid this error, some rules of scientific knowledge are needed. Growth, Employment, Inequality, and the Environment presents a method that contains such rules. The method is derived from the Popperian epistemology, making it operational in economics. The proposed unified theory is therefore empirically valid; it is a good approximation of the real world. Theoretical economics is thus treated under explicit epistemological rules: theory is the servant, not the master.

PART I

Scientific Knowledge Rules, Facts, and Standard Theories Refutation

CHAPTER 1

Popperian Epistemology in Economics: The Alpha-Beta Method

Why has the progress of scientific knowledge in economics proceeded at a pace that is slower than that of the natural sciences? A possible reason is the limitation of data, in quantity and quality; in addition, the instruments of measurement for the social phenomena are relatively imperfect. The other reason seems to rest upon the role of methodology in the construction of scientific knowledge in economics. Compared to physics, economics seeks to explain the functioning of the social world, which is a much more complex world than the physical world; one may then propose the principle that understanding more complex worlds, such as the social world, is more demanding on methodology than understanding the physical world.

Economist Paul Samuelson wrote in his classic book Foundations of Economic Analysis about this principle as follows:

[This] book may hold some interest for the reader who is curious about the methodology of the social sciences . . . [I]n a hard, exact science [as physics] a practitioner does not really have to know much about methodology. Indeed, even if he is a definitely misguided methodologist, the subject itself has a self-cleansing property which renders harmless his aberrations. By contrast, a scholar in economics who is fundamentally confused concerning [methodology] may spend a lifetime shadow-boxing with reality. In a sense, therefore, in order to earn his daily bread as a fruitful contributor to knowledge, the practitioner of an intermediately hard science like economics must come to terms with methodological problems. (1947, pp. viii–ix)

If the separation between physics and economics made by Samuelson as hard and intermediately hard sciences was transformed into complex (economics) and less complex (physics), as will be done in this book, then the principle proposed above would follow. Economics is a more methodology-intensive science than physics.

Methodology is another name for epistemology (from the Greek episteme, knowledge). Epistemology deals with the logic of scientific knowledge, from which a practical set of rules to arrive at scientific knowledge can be derived. Such a set of rules is needed in economics. Popperian epistemology will be used for this purpose, from which the set of scientific rules—called the alpha-beta method—will be derived. The method is presented in this chapter and will then be applied in the entire book. (This chapter draws heavily on Figueroa [2012].)

Deriving Scientific Rules from Popperian Epistemology

Scientific knowledge seeks to establish relations among objects and explain them. The objects can be mental or physical. Formal sciences study the relations among mental objects, whereas factual sciences study the relations among material objects. Mathematics and logic are examples of formal sciences; physics and economics are instances of factual sciences.

Scientific knowledge can be seen as a set of propositions that is error free. What would be the criterion to accept or reject a proposition as scientific? It depends upon the type of science. In the formal sciences, the criterion seems to be rather straightforward: the relations established must be free of internal logical contradictions. In the factual sciences, by contrast, the criteria are more involved. The propositions of a factual science must be free of internal logical contradictions as well. However, this criterion constitutes just a necessary condition; empirical consistency between the propositions and the facts will also be required. The real world cannot be explained by using deductive logic alone. This is the logic that corresponds to formal sciences, not to factual sciences.

According to the epistemology developed by Karl Popper (1968), scientific knowledge that seeks to explain the real world cannot be attained by using inductive logic. There is no such thing as inductive logic, that is, there is no logical way to go from particular empirical observations to general relations. His classical example is: no matter how many instances of white swans we may have observed, this does not justify the conclusion that all swans are white.

The logic of scientific knowledge developed by Popper can be summarized as follows: Theory (an abstract world) is needed to explain the real world; from theory, some conclusions about reality are derived by logical deduction. These conclusions, the empirical predictions of the theory, can then be submitted for confrontation against reality. If the empirical predictions and reality are consistent, we have no reason to discard the theory; if they are inconsistent, then the theory has been proven false and it has been falsified. Hence, a scientific theory must be able to generate falsifiable or refutable empirical propositions.

An empirical proposition is falsifiable if in principle it can be false. This is the principle of falsification. For example, the proposition “It will rain or not rain here tomorrow” is not falsifiable, whereas the proposition “It will rain here tomorrow” is. Falsification is therefore the demarcation principle between scientific and nonscientific propositions.

The basic scientific rules that can be logically derived from Popperian epistemology include:

(a) Scientific theory is required to explain the real world. No theory, no explanation.

(b) Falsification is the criterion of demarcation. The scientific theory must be falsifiable in the sense that the empirical propositions derived from it, by deductive logic, must be, in principle, false.

(c) The theory is rejected if its empirical predictions are refuted by facts; if they are not, the theory is accepted provisionally until new data or superior scientific theory appears.

The question now is to see whether these scientific rules can be applied to economics. In order to answer this question, we must first be clear about the scope of economics.

The Economic Process

Economics is a social science. It seeks to explain a particular aspect of the functioning of human societies: the determinants of the production of goods and its distribution between social groups. Economics studies those social relations that are related to the production and distribution of goods. Goods constitute the cement that links people in social relations. This is the standard scope of economics.

Human societies constitute complex realities. The notion of complexity can be defined by the existence of a large number of elements and the heterogeneity among them that forms the reality, together with the multiple factors that shape the relations between those elements. Human diversity, together with the multiplicity of human interactions, makes human societies intricate realities. The simple fact that individuals in a human society are not identical, as compared to the homogeneity of atoms in the physical world, suggests that the social world is more complex than the physical world. Human societies are complex systems of interacting individuals in which individuals themselves are complex systems.

How can a complex social reality be subject to scientific knowledge? The usual answer is to use the method of abstraction. This method rests on two particular assumptions. The first assumption is that the complex social reality can be transformed into a process, in which the social relations occur regularly and repeatedly. The second is that the complex social reality is reducible to a simpler abstract world by setting aside elements of the process that are not important to understand the social world.

The concept of process to be used in this book refers to the one developed by economist Nicholas Georgescu-Roegen (1971, Chapter 9). A process is as a series of social activities carried out in parts of the real world, having a given duration (a given unit of time) and a purpose, and repeated period after period. The essential characteristics of a process then include the existence of a boundary that separates the outside world from the inside because the process refers to a partial aspect of social reality. Moreover, there are elements that cross the boundary from outside the process—called the exogenous elements—and those that cross the boundary from inside the process—the endogenous elements. In a process, there is also an underlying mechanism by which the exogenous elements influence the outcome of the endogenous elements. The other characteristic is repetition: process always implies repetition in which the unit of time is well defined.

The analytical transformation of a complex social reality into a process implies the separation of all elements of reality into endogenous and exogenous. The complete list of endogenous and exogenous elements of a process would include observable and nonobservable elements. When observable, call these elements endogenous variables and exogenous variables. The method of abstraction must now be applied to reduce the full process to a simpler process, which is called process analysis. The use of abstraction implies the selection of the most significant endogenous and exogenous variables together with the most significant mechanisms of the process.

Certainly, to present the full process, with a complete list of the variables, would be equivalent to constructing a map of reality to the scale 1:1. As in the case of the map, a complex reality cannot be understood at this scale of representation. Abstraction implies that some variables and some mechanisms must be ignored. This is how a real world is transformed into an abstract process, into an abstract world, in which only the supposedly important variables and mechanisms are included and the rest are just ignored.

Figure 1.1 Diagrammatic representation of process analysis.

Figure 1.1 shows the analytical representation of process analysis. The segment t_o–t₁ represents the duration of the process (the unit of time), which is going to be repeated period after period. X is the set of exogenous variables and Y is the set of endogenous variables. The shaded area indicates the underlying mechanism by which X and Y are connected.

What happens inside the process is unobservable, as indicated by the shaded area in the figure. If it were observable, the interior of the process would have to be considered as another process in itself, with other endogenous and exogenous variables and another mechanism; but the latter mechanism would also be observable and then constitute another process, and so on. Thus, we would arrive at the logical problem of an infinite regress. (Science avoids this trap by making assumptions about the initial conditions of a process.) Ultimately, there must be something hidden beneath the things we observe, which are contained in the mechanisms of the process. Science seeks to unravel those underlying factors.

Because a process repeats itself period after period, the relationships between exogenous and endogenous variables can be observed continuously and thereby systematic relationships or empirical regularities can be observed. The existence of empirical regularities is a necessary condition for scientific knowledge. A chaotic world—where regularities are absent—is much harder to understand; therefore, it will not be a part of process analysis.

How do we decide which elements are important in a process and which are not? This is established by a set of assumptions, that is, by constructing a scientific theory. A scientific theory is a set of assumptions about the workings of the abstract world, which intends to resemble well the real world.

The endogenous variables constitute the ...

Table of contents

• Cover
• Title
• Part I   Scientific Knowledge Rules, Facts, and Standard Theories Refutation
• Part II   The Short Run: Production, Employment, and Distribution
• Notes
• Bibliography
• Index